public void tf_equal_test()
{
using (var K = new TensorFlowBackend())
{
var x = K.variable(array: new double[, ] {
{ 1, 2, 3 }, { 4, 5, 6 }
});
var y = K.variable(array: new double[, ] {
{ 1, 2, 3 }, { 4, 5, 6 }
});
Assert.AreEqual(new bool[, ] {
{ true, true, true }, { true, true, true }
}, (bool[, ])K.equal(x, y).eval());
x = K.variable(array: new double[, ] {
{ 1, 2, 3 }, { 4, 5, 6 }
});
y = K.variable(array: new double[, ] {
{ 1, 2, 3 }, { 4, 5, 0 }
});
Assert.AreEqual(new bool[, ] {
{ true, true, true }, { true, true, false }
}, (bool[, ])K.equal(x, y).eval());
}
}
public void tf_conv_test()
{
using (var K = new TensorFlowBackend())
{
Array input = Matrix.Magic(5);
input = input.ExpandDimensions(0);
input = input.ExpandDimensions(3);
Array kernel = new double[, ]
{
{ 1, 2 },
{ 3, 4 },
};
kernel = kernel.ExpandDimensions(2);
kernel = kernel.ExpandDimensions(3);
Tensor input_var = K.variable(array: input, dtype: DataType.Float);
Tensor kernel_var = K.variable(array: kernel, dtype: DataType.Float);
Tensor conv = K.conv2d(input_var, kernel_var, new[] { 1, 1, 1, 1 }, PaddingType.Valid);
float[,,,] output = (float[, , , ])conv.eval();
Assert.AreEqual(new double[, ] {
{ 1, 2, 3, 4 }
}, output);
}
}
public void tf_mean_test()
{
using (var K = new TensorFlowBackend())
{
var x = K.variable(array: new double[, ] {
{ 1, 2, 3 }, { 4, 5, 6 }
}, dtype: DataType.Double);
{
double a = (double)K.mean(x, axis: new[] { 0, 1 }).eval();
Assert.AreEqual(3.5, a);
double[] b = (double[])K.mean(x, axis: new[] { 0 }).eval();
Assert.AreEqual(new[] { 2.5, 3.5, 4.5 }, b);
double[] c = (double[])K.mean(x, axis: new[] { 1 }).eval();
Assert.AreEqual(new[] { 2.0, 5.0 }, c);
double[,] d = (double[, ])K.mean(x, axis: new int[] { }).eval();
Assert.AreEqual(new double[, ] {
{ 1, 2, 3 }, { 4, 5, 6 }
}, d);
}
{
double[] a = (double[])K.mean(x, axis: -1).eval();
Assert.AreEqual(new[] { 2, 5 }, a);
double[] b = (double[])K.mean(x, axis: 0).eval();
Assert.AreEqual(new[] { 2.5, 3.5, 4.5 }, b);
double[] c = (double[])K.mean(x, axis: 1).eval();
Assert.AreEqual(new[] { 2.0, 5.0 }, c);
}
}
}
public void tf_sum_test()
{
using (var K = new TensorFlowBackend())
{
var x = K.variable(array: new double[, ] {
{ 1, 2, 3 }, { 4, 5, 6 }
}, dtype: DataType.Double);
{
double a = (double)K.sum(x, axis: new[] { 0, 1 }).eval();
Assert.AreEqual(21, a);
double[] b = (double[])K.sum(x, axis: new[] { 0 }).eval();
Assert.AreEqual(new[] { 5.0, 7.0, 9.0 }, b);
double[] c = (double[])K.sum(x, axis: new[] { 1 }).eval();
Assert.AreEqual(new[] { 6.0, 15.0 }, c);
double[,] d = (double[, ])K.sum(x, axis: new int[] { }).eval();
Assert.AreEqual(new double[, ] {
{ 1, 2, 3 }, { 4, 5, 6 }
}, d);
}
{
double[] a = (double[])K.sum(x, axis: -1).eval();
Assert.AreEqual(new[] { 6.0, 15.0 }, a);
double[] b = (double[])K.sum(x, axis: 0).eval();
Assert.AreEqual(new[] { 5.0, 7.0, 9.0 }, b);
double[] c = (double[])K.sum(x, axis: 1).eval();
Assert.AreEqual(new[] { 6.0, 15.0 }, c);
}
}
}
public void tf_partial_shape_test()
{
// Note: Keras/TensorFlow represent unknown dimensions
// as None, whereas TensorFlowSharp represents as -1:
/*
* import keras
*
* from keras.models import Sequential
* from keras.layers import Dense
* from keras import backend as K
* import numpy as np
*
* a = K.placeholder(shape = (None, 2))
* b = K.variable(np.matrix([[ 1, 2, 3], [4, 5, 6]]))
* ab = K.dot(a, b)
*
* shape_a = K.int_shape(a)
* shape_b = K.int_shape(b)
* shape_ab = K.int_shape(ab)
*
* print(shape_a)
* print(shape_b)
* print(shape_ab)
*
* >>> Using TensorFlow backend.
* (None, 2)
* (2, 3)
* (None, 3)
*/
using (var K = new TensorFlowBackend())
{
Tensor a = K.placeholder(shape: new int?[] { null, 2 });
Tensor b = K.variable(array: new float[, ] {
{ 1, 2, 3 },
{ 4, 5, 6 }
});
var ab = K.dot(a, b);
int?[] shape_a = K.int_shape(a);
int?[] shape_b = K.int_shape(b);
int?[] shape_ab = K.int_shape(ab);
long[] tf_shape_a = K.In(a).TF_Shape;
long[] tf_shape_b = K.In(b).TF_Shape;
long[] tf_shape_ab = K.In(ab).TF_Shape;
AssertEx.AreEqual(new int?[] { null, 2 }, shape_a);
AssertEx.AreEqual(new int?[] { 2, 3 }, shape_b);
AssertEx.AreEqual(new int?[] { null, 3 }, shape_ab);
AssertEx.AreEqual(new long[] { -1, 2 }, tf_shape_a);
AssertEx.AreEqual(new long[] { 2, 3 }, tf_shape_b);
AssertEx.AreEqual(new long[] { -1, 3 }, tf_shape_ab);
}
}
public void tf_argmax_test()
{
using (var K = new TensorFlowBackend())
{
var x = K.variable(array: new double[, ] {
{ 1, 9, 3 }, { 9, 5, 6 }
});
Assert.AreEqual(new double[] { 1, 0, 1 }, K.argmax(x, axis: 0).eval());
Assert.AreEqual(new double[] { 1, 0 }, K.argmax(x, axis: 1).eval());
Assert.AreEqual(new double[] { 1, 0 }, K.argmax(x).eval());
}
}
public void tf_reshape_test()
{
using (var K = new TensorFlowBackend())
{
double[,] input_array = new double[, ] {
{ 1, 2 }, { 3, 4 }
};
Tensor variable = K.variable(array: input_array, dtype: DataType.Double);
Tensor variable_new_shape = K.reshape(variable, new int[] { 1, 4 });
double[,] output = (double[, ])variable_new_shape.eval();
Assert.AreEqual(new double[, ] {
{ 1, 2, 3, 4 }
}, output);
}
}
public void tf_toString_with_names()
{
using (var K = new TensorFlowBackend())
{
var input = K.placeholder(shape: new int?[] { 2, 4, 5 }, name: "a");
double[,] val = new double[, ] {
{ 1, 2 }, { 3, 4 }
};
var kvar = K.variable(array: (Array)val, dtype: DataType.Double, name: "b");
string a = input.ToString();
string b = kvar.ToString();
Assert.AreEqual("KerasSharp.Engine.Topology.Tensor 'a_0' shape=[2, 4, 5] dtype=Float", a);
Assert.AreEqual("KerasSharp.Engine.Topology.Tensor 'b_0' shape=[2, 2] dtype=Double", b);
}
}
public void toString()
{
using (var K = new TensorFlowBackend())
{
var input = K.placeholder(shape: new int?[] { 2, 4, 5 });
double[,] val = new double[, ] {
{ 1, 2 }, { 3, 4 }
};
var kvar = K.variable(array: (Array)val);
string a = input.ToString();
string b = kvar.ToString();
Assert.AreEqual("KerasSharp.Engine.Topology.Tensor 'Placeholder0_0' shape=[2, 4, 5] dtype=Float", a);
Assert.AreEqual("KerasSharp.Engine.Topology.Tensor 'Const0_0' shape=[2, 2] dtype=Double", b);
}
}
public void tf_int_shape()
{
using (var K = new TensorFlowBackend())
{
#region doc_int_shape
var input = K.placeholder(shape: new int?[] { 2, 4, 5 });
var a = K.int_shape(input); // (2, 4, 5)
var val = new[, ] {
{ 1, 2 }, { 3, 4 }
};
var kvar = K.variable(array: val);
var b = K.int_shape(kvar); //(2, 2)
#endregion
Assert.AreEqual(new[] { 2, 4, 5 }, a);
Assert.AreEqual(new[] { 2, 2 }, b);
}
}
public void tf_ndim_test()
{
// https://github.com/fchollet/keras/blob/f65a56fb65062c8d14d215c9f4b1015b97cc5bf3/keras/backend/tensorflow_backend.py#L508
using (var K = new TensorFlowBackend())
{
#region doc_ndim
var input = K.placeholder(shape: new int?[] { 2, 4, 5 });
double[,] val = new double[, ] {
{ 1, 2 }, { 3, 4 }
};
var kvar = K.variable(array: (Array)val);
int?a = K.ndim(input); // 3
int?b = K.ndim(kvar); // 2
#endregion
Assert.AreEqual(3, a);
Assert.AreEqual(2, b);
}
}
public void tf_variable_test()
{
using (var K = new TensorFlowBackend())
{
#region doc_variable
double[,] val = new double[, ] {
{ 1, 2 }, { 3, 4 }
};
var kvar = K.variable(array: (Array)val, dtype: DataType.Double, name: "example_var");
var a = K.dtype(kvar); // 'float64'
var b = kvar.eval(); // { { 1, 2 }, { 3, 4 } }
#endregion
Assert.AreEqual(DataType.Double, a);
Assert.AreEqual(new double[, ] {
{ 1, 2 }, { 3, 4 }
}, b);
}
}
public void tf_softmax_test()
{
using (var K = new TensorFlowBackend())
{
double[,] a = new double[, ] {
{ -4, 2 }, { 0.02, 0.3 }
};
var ta = K.variable(array: (Array)a, name: "example_var", dtype: DataType.Double);
var tr = K.softmax(ta);
double[,] r = (double[, ])tr.eval();
AssertEx.AreEqual(new double[, ] {
{ 0.0024726231566347748, 0.99752737684336534 },
{ 0.430453776060771, 0.56954622393922893 }
}, r, 1e-8);
AssertEx.AreEqual(r.GetRow(0), Accord.Math.Special.Softmax(a.GetRow(0)), 1e-8);
AssertEx.AreEqual(r.GetRow(1), Accord.Math.Special.Softmax(a.GetRow(1)), 1e-8);
}
}
